ALPR (Automatic License Plate Recognition) system often functions as the core module of “intelligent” transportation infrastructure applications. License plate recognition can be employed to identify a vehicle by automatically reading a license plate utilizing an image processing and character recognition technology. A license plate recognition operation can be performed by locating the license plate in an image, segmenting the characters in the plate, and performing an OCR (Optical Character Recognition) operation with respect to the characters identified. Performance requirements for an ALPR engine are increasing over time driven by users' desire to reduce the number of recognition errors. One common source of errors is a close-character sibling error where certain characters look very similar to each other and in the presence of image noises, lead to incorrect classification by the ALPR engine. Examples include 8/B, 0/D, 2/Z, 5/S, etc. These most often occur on the letter-number boundary where numbers look like letters and vice versa. If ‘O’ or ‘Q’ is part of the OCR dictionary, then the confusion is typically between ‘O’, ‘Q’, and ‘D’.
The OCR subsystem typically accepts individual character images as input and runs algorithms to classify the image as one of 32-36 (depending on jurisdiction) class labels such as A-Z and 0-9. FIGS. 1A and 1B illustrate the performance of a trained OCR engine when tested on an independent test set of characters. The font excludes ‘IOQU’ and thus there are only 32 possible classes. The ground truth labels are on the X-axis and the OCR conclusion is on the Y-axis. This matrix is called a “confusion matrix” since the characters that are confused with each other by the system become readily apparent.
For a perfect OCR engine, all off diagonal entries of the confusion matrix are zero. In FIGS. 1A and 1B, the two highlighted cases of OCR errors are ‘B’ incorrectly recognized as ‘8’ (‘B/8’) 15 times and ‘D’ incorrectly recognized as ‘0’ (‘D/0’) a total of 16 times. These are the expected dose character errors and the combinations change depending on the font. Of note is that the reverse combinations (‘8/B’) and (‘0/D’) have 2 and 1 errors respectfully indicating an asymmetry in confusion. Example character images extracted from the license plate images are shown in FIG. 2. Under poor imaging conditions, a blurry ‘D’ loses its top and bottom left corner distinctions and begins to look very similar to a ‘0’. Similarly, a blurry ‘B’ appears and is often incorrectly classified as an ‘8’ whereas a blurry ‘0’ or ‘8’ still looks like ‘0’ and an ‘8’ are seldom misclassified. This explains the asymmetry in error rates observed in FIGS. 1A and 1B.
Majority of prior art methods utilize a higher resolution camera and increased illumination to distinguish between the close-characters siblings. Such methods require a large amount of expert hand tuning of camera setup parameters in order to achieve and maintain image quality levels enabling the ALPR engine to distinguish between the dose character siblings. License plate image signatures or image hashes have also been employed to augment the ALPR engine to improve the accuracy in license plates that have close-character siblings. Such approach requires manual plate recognition the first time each plate is checked by the system, an accurate automatic tight cropping of each license plate, access to a continuously updated central database, and a completely separate processing pipeline.
Another prior art approach places a barcode on the license plate in order to facilitate automatic reading of the plate, however, this approach occupies more valuable space on the plate and can lead to less visually appealing plates. Additionally, the barcode occupies significant space in order to make the barcode robust to the imaging noises present in the license plate reading systems and in order to carry its data payload which needs to include the plate character sequence and the state information. The bar code may also be visually unappealing depending on the technology.
FIG. 3 illustrates license plate image 180 illustrating character segmentation in determining the first and last characters as the spacing of characters is arbitrary. In FIG. 3, the plate cover to the left of the ‘N’ and to the right of the ‘H’ depicted by arrows 190 and 195 can be mistakenly segmented and classified as a ‘1’ adding either one or two characters to the plate code. Additionally, the location of the logo can vary in size, location, and shape from state-to-state and across different plate designs are erroneously identified as a valid segmented character or by “attaching” itself to one of its neighboring characters in the segmentation results.
Based on the foregoing, it is believed that a need exists for an improved method and system for providing a license plate overlay decal with an infrared annotation mark for optical character recognition and segmentation, as will be described in greater detail herein.